Abstract

Abstract—Electroencephalogram (EEG) recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable signal variations due to artifacts or recognizer-subject feedback. In this paper, we propose a comparison of processing method (i.e., NPCA, JADE, and SOBI) entailing time-series EEG signals. Finally, the promising results reported here (up to 94% average classification accuracy and 36.4% improvement of maximum average transfer rate) reflect the considerable potential of EEG for the continuous classification of mental states.